def luminous_efficacy(sd: SpectralDistribution,
lef: Optional[SpectralDistribution] = None) -> Floating:
"""
Return the *luminous efficacy* in :math:`lm\\cdot W^{-1}` of given
spectral distribution using given luminous efficiency function.
Parameters
----------
sd
test spectral distribution
lef
:math:`V(\\lambda)` luminous efficiency function, default to the
*CIE 1924 Photopic Standard Observer*.
Returns
-------
:class:`numpy.floating`
Luminous efficacy in :math:`lm\\cdot W^{-1}`.
References
----------
:cite:`Wikipedia2005c`
Examples
--------
>>> from colour import SDS_LIGHT_SOURCES
>>> sd = SDS_LIGHT_SOURCES['Neodimium Incandescent']
>>> luminous_efficacy(sd) # doctest: +ELLIPSIS
136.2170803...
"""
efficacy = CONSTANT_K_M * luminous_efficiency(sd, lef)
return as_float_scalar(efficacy)
def colour_rendering_indexes(
test_data: Tuple[TCS_ColorimetryData, ...],
reference_data: Tuple[TCS_ColorimetryData, ...],
) -> Dict[Integer, TCS_ColourQualityScaleData]:
"""
Return the *test colour samples* rendering indexes :math:`Q_a`.
Parameters
----------
test_data
Test data.
reference_data
Reference data.
Returns
-------
:class:`dict`
*Test colour samples* *Colour Rendering Index* (CRI).
"""
Q_as = {}
for i in range(len(test_data)):
Q_as[i + 1] = TCS_ColourQualityScaleData(
test_data[i].name,
100
- 4.6
* as_float_scalar(
euclidean_distance(reference_data[i].UVW, test_data[i].UVW)
),
)
return Q_as
def luminous_flux(
sd: SpectralDistribution,
lef: Optional[SpectralDistribution] = None,
K_m: Floating = CONSTANT_K_M,
) -> Floating:
"""
Return the *luminous flux* for given spectral distribution using given
luminous efficiency function.
Parameters
----------
sd
test spectral distribution
lef
:math:`V(\\lambda)` luminous efficiency function, default to the
*CIE 1924 Photopic Standard Observer*.
K_m
:math:`lm\\cdot W^{-1}` maximum photopic luminous efficiency.
Returns
-------
:class:`numpy.floating`
Luminous flux.
References
----------
:cite:`Wikipedia2003b`
Examples
--------
>>> from colour import SDS_LIGHT_SOURCES
>>> sd = SDS_LIGHT_SOURCES['Neodimium Incandescent']
>>> luminous_flux(sd) # doctest: +ELLIPSIS
23807.6555273...
"""
lef = cast(
SpectralDistribution,
optional(lef,
SDS_LEFS_PHOTOPIC["CIE 1924 Photopic Standard Observer"]),
)
lef = reshape_sd(
lef,
sd.shape,
extrapolator_kwargs={
"method": "Constant",
"left": 0,
"right": 0
},
)
flux = K_m * np.trapz(lef.values * sd.values, sd.wavelengths)
return as_float_scalar(flux)
def bandwidth_FWHM(self, value: Optional[Floating]):
"""Setter for the **self.bandwidth_FWHM** property."""
if value is not None:
attest(
is_numeric(value),
f'"bandwidth_FWHM" property: "{value}" is not a "number"!',
)
value = as_float_scalar(value)
self._bandwidth_FWHM = value
def colour_quality_scales(
test_data: Tuple[VS_ColorimetryData, ...],
reference_data: Tuple[VS_ColorimetryData, ...],
scaling_f: Floating,
CCT_f: Floating,
) -> Dict[Integer, VS_ColourQualityScaleData]:
"""
Return the *VS test colour samples* rendering scales.
Parameters
----------
test_data
Test data.
reference_data
Reference data.
scaling_f
Scaling factor constant.
CCT_f
Factor penalizing lamps with extremely low correlated colour
temperatures.
Returns
-------
:class:`dict`
*VS Test colour samples* colour rendering scales.
"""
Q_as = {}
for i in range(len(test_data)):
D_C_ab = test_data[i].C - reference_data[i].C
D_E_ab = as_float_scalar(
euclidean_distance(test_data[i].Lab, reference_data[i].Lab))
if D_C_ab > 0:
D_Ep_ab = np.sqrt(D_E_ab**2 - D_C_ab**2)
else:
D_Ep_ab = D_E_ab
Q_a = scale_conversion(D_Ep_ab, CCT_f, scaling_f)
Q_as[i + 1] = VS_ColourQualityScaleData(test_data[i].name, Q_a, D_C_ab,
D_E_ab, D_Ep_ab)
return Q_as
def uv_to_UCS(uv: ArrayLike, V: Floating = 1) -> NDArray:
"""
Return the *CIE 1960 UCS* colourspace array from given *uv* chromaticity
coordinates.
Parameters
----------
uv
*uv* chromaticity coordinates.
V
Optional :math:`V` *luminance* value used to construct the
*CIE 1960 UCS* colourspace array, the default :math:`V` *luminance* is
set to 1.
Returns
-------
:class:`numpy.ndarray`
*CIE 1960 UCS* colourspace array.
References
----------
:cite:`Wikipedia2008c`
Examples
--------
>>> import numpy as np
>>> uv = np.array([0.37720213, 0.33413508])
>>> uv_to_UCS(uv) # doctest: +ELLIPSIS
array([ 1.1288911..., 1. , 0.8639104...])
"""
u, v = tsplit(uv)
V = as_float_scalar(to_domain_1(V))
UVW = tstack([V * u / v, full(u.shape, V), -V * (u + v - 1) / v])
return from_range_1(UVW)
def cctf_decoding_RIMMRGB(
X_p: Union[FloatingOrArrayLike, IntegerOrArrayLike],
bit_depth: Integer = 8,
in_int: Boolean = False,
E_clip: Floating = 2.0,
) -> FloatingOrNDArray:
"""
Define the *RIMM RGB* decoding colour component transfer function
(Encoding CCTF).
Parameters
----------
X_p
Non-linear data :math:`X'_{RIMM}`.
bit_depth
Bit depth used for conversion.
in_int
Whether to treat the input value as integer code value or float
equivalent of a code value at a given bit depth.
E_clip
Maximum exposure level.
Returns
-------
:class:`numpy.floating` or :class:`numpy.ndarray`
Linear data :math:`X_{RIMM}`.
Notes
-----
+----------------+-----------------------+---------------+
| **Domain \\*** | **Scale - Reference** | **Scale - 1** |
+================+=======================+===============+
| ``X_p`` | [0, 1] | [0, 1] |
+----------------+-----------------------+---------------+
+----------------+-----------------------+---------------+
| **Range \\*** | **Scale - Reference** | **Scale - 1** |
+================+=======================+===============+
| ``X`` | [0, 1] | [0, 1] |
+----------------+-----------------------+---------------+
\\* This definition has an input integer switch, thus the domain-range
scale information is only given for the floating point mode.
References
----------
:cite:`Spaulding2000b`
Examples
--------
>>> cctf_decoding_RIMMRGB(0.291673732475746) # doctest: +ELLIPSIS
0.1...
>>> cctf_decoding_RIMMRGB(74, in_int=True) # doctest: +ELLIPSIS
0.1...
"""
X_p = to_domain_1(X_p)
I_max = as_float_scalar(2**bit_depth - 1)
if not in_int:
X_p = X_p * I_max
V_clip = 1.099 * spow(E_clip, 0.45) - 0.099
m = V_clip * X_p / I_max
with domain_range_scale("ignore"):
X = np.where(
X_p / I_max
< cctf_encoding_RIMMRGB(0.018, bit_depth, E_clip=E_clip),
m / 4.5,
spow((m + 0.099) / 1.099, 1 / 0.45),
)
return as_float(from_range_1(X))
def hull_section(
hull: trimesh.Trimesh, # type: ignore[name-defined] # noqa
axis: Union[Literal["+z", "+x", "+y"], str] = "+z",
origin: Floating = 0.5,
normalise: Boolean = False,
) -> NDArray:
"""
Compute the hull section for given axis at given origin.
Parameters
----------
hull
*Trimesh* hull.
axis
Axis the hull section will be normal to.
origin
Coordinate along ``axis`` at which to plot the hull section.
normalise
Whether to normalise ``axis`` to the extent of the hull along it.
Returns
-------
:class:`numpy.ndarray`
Hull section vertices.
Examples
--------
>>> from colour.geometry import primitive_cube
>>> from colour.utilities import is_trimesh_installed
>>> vertices, faces, outline = primitive_cube(1, 1, 1, 2, 2, 2)
>>> if is_trimesh_installed:
... import trimesh
... hull = trimesh.Trimesh(vertices['position'], faces, process=False)
... hull_section(hull, origin=0)
array([[-0. , -0.5, 0. ],
[ 0.5, -0.5, 0. ],
[ 0.5, 0. , -0. ],
[ 0.5, 0.5, -0. ],
[-0. , 0.5, 0. ],
[-0.5, 0.5, 0. ],
[-0.5, 0. , -0. ],
[-0.5, -0.5, -0. ],
[-0. , -0.5, 0. ]])
"""
import trimesh
axis = validate_method(
axis,
["+z", "+x", "+y"],
'"{0}" axis is invalid, it must be one of {1}!',
)
if axis == "+x":
normal, plane = np.array([1, 0, 0]), np.array([origin, 0, 0])
elif axis == "+y":
normal, plane = np.array([0, 1, 0]), np.array([0, origin, 0])
elif axis == "+z":
normal, plane = np.array([0, 0, 1]), np.array([0, 0, origin])
if normalise:
vertices = hull.vertices * normal
origin = as_float_scalar(
linear_conversion(
origin, [0, 1],
[np.min(vertices), np.max(vertices)]))
plane[plane != 0] = origin
section = trimesh.intersections.mesh_plane(hull, normal, plane)
if len(section) == 0:
raise ValueError(
f'No section exists on "{axis}" axis at {origin} origin!')
section = close_chord(unique_vertices(edges_to_chord(section)))
return section
def __init__(self, coefficients: ArrayLike, error: Floating):
self._coefficients = as_float_array(coefficients)
self._error = as_float_scalar(error)
def uv_to_Luv(
uv: ArrayLike,
illuminant: ArrayLike = CCS_ILLUMINANTS[
"CIE 1931 2 Degree Standard Observer"]["D65"],
Y: Floating = 1,
) -> NDArray:
"""
Return the *CIE L\\*u\\*v\\** colourspace array from given :math:`uv^p`
chromaticity coordinates by extending the array last dimension with given
:math:`L` *Lightness*.
Parameters
----------
uv
:math:`uv^p` chromaticity coordinates.
illuminant
Reference *illuminant* *CIE xy* chromaticity coordinates or *CIE xyY*
colourspace array.
Y
Optional :math:`Y` *luminance* value used to construct the intermediate
*CIE XYZ* colourspace array, the default :math:`Y` *luminance* value is
1.
Returns
-------
:class:`numpy.ndarray`
*CIE L\\*u\\*v\\** colourspace array.
Notes
-----
+----------------+-----------------------+-----------------+
| **Range** | **Scale - Reference** | **Scale - 1** |
+================+=======================+=================+
| ``Luv`` | ``L`` : [0, 100] | ``L`` : [0, 1] |
| | | |
| | ``u`` : [-100, 100] | ``u`` : [-1, 1] |
| | | |
| | ``v`` : [-100, 100] | ``v`` : [-1, 1] |
+----------------+-----------------------+-----------------+
| ``illuminant`` | [0, 1] | [0, 1] |
+----------------+-----------------------+-----------------+
References
----------
:cite:`CIETC1-482004j`
Examples
--------
>>> import numpy as np
>>> uv = np.array([0.37720213, 0.50120264])
>>> uv_to_Luv(uv) # doctest: +ELLIPSIS
array([ 100. , 233.1837603..., 42.7474385...])
"""
u, v = tsplit(uv)
Y = as_float_scalar(to_domain_1(Y))
X = 9 * u / (4 * v)
Z = (-5 * Y * v - 3 * u / 4 + 3) / v
XYZ = tstack([X, full(u.shape, Y), Z])
return XYZ_to_Luv(from_range_1(XYZ), illuminant)
def colour_fidelity_index_CIE2017(
sd_test: SpectralDistribution,
additional_data: Boolean = False
) -> Union[Floating, ColourRendering_Specification_CIE2017]:
"""
Return the *CIE 2017 Colour Fidelity Index* (CFI) :math:`R_f` of given
spectral distribution.
Parameters
----------
sd_test
Test spectral distribution.
additional_data
Whether to output additional data.
Returns
-------
:class:`numpy.floating` or \
:class:`colour.quality.ColourRendering_Specification_CIE2017`
*CIE 2017 Colour Fidelity Index* (CFI) :math:`R_f`.
References
----------
:cite:`CIETC1-902017`
Examples
--------
>>> from colour.colorimetry import SDS_ILLUMINANTS
>>> sd = SDS_ILLUMINANTS['FL2']
>>> colour_fidelity_index_CIE2017(sd) # doctest: +ELLIPSIS
70.1208254...
"""
if sd_test.shape.start > 380 or sd_test.shape.end < 780:
usage_warning("Test spectral distribution shape does not span the"
"recommended 380-780nm range, missing values will be"
"filled with zeros!")
# NOTE: "CIE 2017 Colour Fidelity Index" standard recommends filling
# missing values with zeros.
sd_test = cast(SpectralDistribution, sd_test.copy())
sd_test.extrapolator = Extrapolator
sd_test.extrapolator_kwargs = {
"method": "constant",
"left": 0,
"right": 0,
}
if sd_test.shape.interval > 5:
raise ValueError("Test spectral distribution interval is greater than"
"5nm which is the maximum recommended value "
'for computing the "CIE 2017 Colour Fidelity Index"!')
shape = SpectralShape(
SPECTRAL_SHAPE_CIE2017.start,
SPECTRAL_SHAPE_CIE2017.end,
sd_test.shape.interval,
)
CCT, D_uv = tsplit(CCT_reference_illuminant(sd_test))
sd_reference = sd_reference_illuminant(CCT, shape)
# NOTE: All computations except CCT calculation use the
# "CIE 1964 10 Degree Standard Observer".
# pylint: disable=E1102
cmfs_10 = reshape_msds(MSDS_CMFS["CIE 1964 10 Degree Standard Observer"],
shape)
# pylint: disable=E1102
sds_tcs = reshape_msds(load_TCS_CIE2017(shape), shape)
test_tcs_colorimetry_data = tcs_colorimetry_data(sd_test, sds_tcs, cmfs_10)
reference_tcs_colorimetry_data = tcs_colorimetry_data(
sd_reference, sds_tcs, cmfs_10)
delta_E_s = np.empty(len(sds_tcs.labels))
for i, _delta_E in enumerate(delta_E_s):
delta_E_s[i] = euclidean_distance(
test_tcs_colorimetry_data[i].Jpapbp,
reference_tcs_colorimetry_data[i].Jpapbp,
)
R_s = as_float_array(delta_E_to_R_f(delta_E_s))
R_f = as_float_scalar(delta_E_to_R_f(np.average(delta_E_s)))
if additional_data:
return ColourRendering_Specification_CIE2017(
sd_test.name,
sd_reference,
R_f,
R_s,
CCT,
D_uv,
(test_tcs_colorimetry_data, reference_tcs_colorimetry_data),
delta_E_s,
)
else:
return R_f
def test_as_float_scalar(self):
"""Test :func:`colour.utilities.array.as_float_scalar` definition."""
self.assertEqual(as_float_scalar(1), 1.0)
self.assertEqual(as_float_scalar(1).dtype, DEFAULT_FLOAT_DTYPE)
def colour_rendering_index(
sd_test: SpectralDistribution, additional_data: Boolean = False
) -> Union[Floating, ColourRendering_Specification_CRI]:
"""
Return the *Colour Rendering Index* (CRI) :math:`Q_a` of given spectral
distribution.
Parameters
----------
sd_test
Test spectral distribution.
additional_data
Whether to output additional data.
Returns
-------
:class:`numpy.floating` or \
:class:`colour.quality.ColourRendering_Specification_CRI`
*Colour Rendering Index* (CRI).
References
----------
:cite:`Ohno2008a`
Examples
--------
>>> from colour import SDS_ILLUMINANTS
>>> sd = SDS_ILLUMINANTS['FL2']
>>> colour_rendering_index(sd) # doctest: +ELLIPSIS
64.2337241...
"""
# pylint: disable=E1102
cmfs = reshape_msds(
MSDS_CMFS["CIE 1931 2 Degree Standard Observer"],
SPECTRAL_SHAPE_DEFAULT,
)
shape = cmfs.shape
sd_test = reshape_sd(sd_test, shape)
tcs_sds = {sd.name: reshape_sd(sd, shape) for sd in SDS_TCS.values()}
with domain_range_scale("1"):
XYZ = sd_to_XYZ(sd_test, cmfs)
uv = UCS_to_uv(XYZ_to_UCS(XYZ))
CCT, _D_uv = uv_to_CCT_Robertson1968(uv)
if CCT < 5000:
sd_reference = sd_blackbody(CCT, shape)
else:
xy = CCT_to_xy_CIE_D(CCT)
sd_reference = sd_CIE_illuminant_D_series(xy)
sd_reference.align(shape)
test_tcs_colorimetry_data = tcs_colorimetry_data(
sd_test, sd_reference, tcs_sds, cmfs, chromatic_adaptation=True
)
reference_tcs_colorimetry_data = tcs_colorimetry_data(
sd_reference, sd_reference, tcs_sds, cmfs
)
Q_as = colour_rendering_indexes(
test_tcs_colorimetry_data, reference_tcs_colorimetry_data
)
Q_a = as_float_scalar(
np.average(
[v.Q_a for k, v in Q_as.items() if k in (1, 2, 3, 4, 5, 6, 7, 8)]
)
)
if additional_data:
return ColourRendering_Specification_CRI(
sd_test.name,
Q_a,
Q_as,
(test_tcs_colorimetry_data, reference_tcs_colorimetry_data),
)
else:
return Q_a
def colour_quality_scale(
sd_test: SpectralDistribution,
additional_data: Boolean = False,
method: Union[Literal["NIST CQS 7.4", "NIST CQS 9.0"],
str] = "NIST CQS 9.0",
) -> Union[Floating, ColourRendering_Specification_CQS]:
"""
Return the *Colour Quality Scale* (CQS) of given spectral distribution
using given method.
Parameters
----------
sd_test
Test spectral distribution.
additional_data
Whether to output additional data.
method
Computation method.
Returns
-------
:class:`numpy.floating` or \
:class:`colour.quality.ColourRendering_Specification_CQS`
*Colour Quality Scale* (CQS).
References
----------
:cite:`Davis2010a`, :cite:`Ohno2008a`, :cite:`Ohno2013`
Examples
--------
>>> from colour import SDS_ILLUMINANTS
>>> sd = SDS_ILLUMINANTS['FL2']
>>> colour_quality_scale(sd) # doctest: +ELLIPSIS
64.1117031...
"""
method = validate_method(method, COLOUR_QUALITY_SCALE_METHODS)
# pylint: disable=E1102
cmfs = reshape_msds(
MSDS_CMFS["CIE 1931 2 Degree Standard Observer"],
SPECTRAL_SHAPE_DEFAULT,
)
shape = cmfs.shape
sd_test = reshape_sd(sd_test, shape)
vs_sds = {sd.name: reshape_sd(sd, shape) for sd in SDS_VS[method].values()}
with domain_range_scale("1"):
XYZ = sd_to_XYZ(sd_test, cmfs)
uv = UCS_to_uv(XYZ_to_UCS(XYZ))
CCT, _D_uv = uv_to_CCT_Ohno2013(uv)
if CCT < 5000:
sd_reference = sd_blackbody(CCT, shape)
else:
xy = CCT_to_xy_CIE_D(CCT)
sd_reference = sd_CIE_illuminant_D_series(xy)
sd_reference.align(shape)
test_vs_colorimetry_data = vs_colorimetry_data(sd_test,
sd_reference,
vs_sds,
cmfs,
chromatic_adaptation=True)
reference_vs_colorimetry_data = vs_colorimetry_data(
sd_reference, sd_reference, vs_sds, cmfs)
CCT_f: Floating
if method == "nist cqs 9.0":
CCT_f = 1
scaling_f = 3.2
else:
XYZ_r = sd_to_XYZ(sd_reference, cmfs)
XYZ_r /= XYZ_r[1]
CCT_f = CCT_factor(reference_vs_colorimetry_data, XYZ_r)
scaling_f = 3.104
Q_as = colour_quality_scales(
test_vs_colorimetry_data,
reference_vs_colorimetry_data,
scaling_f,
CCT_f,
)
D_E_RMS = delta_E_RMS(Q_as, "D_E_ab")
D_Ep_RMS = delta_E_RMS(Q_as, "D_Ep_ab")
Q_a = scale_conversion(D_Ep_RMS, CCT_f, scaling_f)
if method == "nist cqs 9.0":
scaling_f = 2.93 * 1.0343
else:
scaling_f = 2.928
Q_f = scale_conversion(D_E_RMS, CCT_f, scaling_f)
G_t = gamut_area(
[vs_CQS_data.Lab for vs_CQS_data in test_vs_colorimetry_data])
G_r = gamut_area(
[vs_CQS_data.Lab for vs_CQS_data in reference_vs_colorimetry_data])
Q_g = G_t / GAMUT_AREA_D65 * 100
if method == "nist cqs 9.0":
Q_p = Q_d = None
else:
p_delta_C = np.average([
sample_data.D_C_ab if sample_data.D_C_ab > 0 else 0
for sample_data in Q_as.values()
])
Q_p = as_float_scalar(100 - 3.6 * (D_Ep_RMS - p_delta_C))
Q_d = as_float_scalar(G_t / G_r * CCT_f * 100)
if additional_data:
return ColourRendering_Specification_CQS(
sd_test.name,
Q_a,
Q_f,
Q_p,
Q_g,
Q_d,
Q_as,
(test_vs_colorimetry_data, reference_vs_colorimetry_data),
)
else:
return Q_a
def xy_to_xyY(xy: ArrayLike, Y: Floating = 1) -> NDArray:
"""
Convert from *CIE xy* chromaticity coordinates to *CIE xyY* colourspace by
extending the array last dimension with given :math:`Y` *luminance*.
``xy`` argument with last dimension being equal to 3 will be assumed to be
a *CIE xyY* colourspace array argument and will be returned directly by the
definition.
Parameters
----------
xy
*CIE xy* chromaticity coordinates or *CIE xyY* colourspace array.
Y
Optional :math:`Y` *luminance* value used to construct the *CIE xyY*
colourspace array, the default :math:`Y` *luminance* value is 1.
Returns
-------
:class:`numpy.ndarray`
*CIE xyY* colourspace array.
Notes
-----
+------------+-----------------------+---------------+
| **Domain** | **Scale - Reference** | **Scale - 1** |
+============+=======================+===============+
| ``xy`` | [0, 1] | [0, 1] |
+------------+-----------------------+---------------+
+------------+-----------------------+---------------+
| **Range** | **Scale - Reference** | **Scale - 1** |
+============+=======================+===============+
| ``xyY`` | [0, 1] | [0, 1] |
+------------+-----------------------+---------------+
- This definition is a convenient object provided to implement support of
illuminant argument *luminance* value in various :mod:`colour.models`
package objects such as :func:`colour.Lab_to_XYZ` or
:func:`colour.Luv_to_XYZ`.
References
----------
:cite:`Wikipedia2005`
Examples
--------
>>> xy = np.array([0.54369557, 0.32107944])
>>> xy_to_xyY(xy) # doctest: +ELLIPSIS
array([ 0.5436955..., 0.3210794..., 1. ])
>>> xy = np.array([0.54369557, 0.32107944, 1.00000000])
>>> xy_to_xyY(xy) # doctest: +ELLIPSIS
array([ 0.5436955..., 0.3210794..., 1. ])
>>> xy = np.array([0.54369557, 0.32107944])
>>> xy_to_xyY(xy, 100) # doctest: +ELLIPSIS
array([ 0.5436955..., 0.3210794..., 100. ])
"""
xy = as_float_array(xy)
Y = as_float_scalar(to_domain_1(Y))
# Assuming ``xy`` is actually a *CIE xyY* colourspace array argument and
# returning it directly.
if xy.shape[-1] == 3:
return xy
x, y = tsplit(xy)
xyY = tstack([x, y, full(x.shape, Y)])
return from_range_1(xyY, np.array([1, 1, 100]))
def convert_experiment_results_Breneman1987(
experiment: Literal[1, 2, 3, 4, 6, 8, 9, 11, 12]
) -> CorrespondingColourDataset:
"""
Convert *Breneman (1987)* experiment results to a
:class:`colour.CorrespondingColourDataset` class instance.
Parameters
----------
experiment
*Breneman (1987)* experiment number.
Returns
-------
:class:`colour.CorrespondingColourDataset`
:class:`colour.CorrespondingColourDataset` class instance.
Examples
--------
>>> from pprint import pprint
>>> pprint(tuple(convert_experiment_results_Breneman1987(2)))
... # doctest: +ELLIPSIS
(2,
array([ 0.9582463..., 1. , 0.9436325...]),
array([ 0.9587332..., 1. , 0.4385796...]),
array([[ 388.125 , 405. , 345.625 ],
[ 266.8957925..., 135. , 28.5983365...],
[ 474.5717821..., 405. , 222.75 ...],
[ 538.3899082..., 405. , 24.8944954...],
[ 178.7430167..., 135. , 19.6089385...],
[ 436.6749547..., 405. , 26.5483725...],
[ 124.7746282..., 135. , 36.1965613...],
[ 77.0794172..., 135. , 60.5850563...],
[ 279.9390889..., 405. , 455.8395127...],
[ 149.5808157..., 135. , 498.7046827...],
[ 372.1113689..., 405. , 669.9883990...],
[ 212.3638968..., 135. , 414.6704871...]]),
array([[ 400.1039651..., 405. , 191.7287234...],
[ 271.0384615..., 135. , 13.5 ...],
[ 495.4705323..., 405. , 119.7290874...],
[ 580.7967033..., 405. , 6.6758241...],
[ 190.1933701..., 135. , 7.4585635...],
[ 473.7184115..., 405. , 10.2346570...],
[ 135.4936014..., 135. , 20.2376599...],
[ 86.4689781..., 135. , 35.2281021...],
[ 283.5396281..., 405. , 258.1775929...],
[ 119.7044335..., 135. , 282.6354679...],
[ 359.9532224..., 405. , 381.0031185...],
[ 181.8271461..., 135. , 204.0661252...]]),
1500.0,
1500.0,
0.3,
0.3,
{})
"""
valid_experiment_results = [1, 2, 3, 4, 6, 8, 9, 11, 12]
attest(
experiment in valid_experiment_results,
f'"Breneman (1987)" experiment result is invalid, it must be one of '
f'"{valid_experiment_results}"!',
)
samples_luminance = [
0.270,
0.090,
0.270,
0.270,
0.090,
0.270,
0.090,
0.090,
0.270,
0.090,
0.270,
0.090,
]
experiment_results = list(BRENEMAN_EXPERIMENTS[experiment])
illuminant_chromaticities = experiment_results.pop(0)
Y_r = Y_t = as_float(
BRENEMAN_EXPERIMENT_PRIMARIES_CHROMATICITIES[experiment].Y)
B_r = B_t = 0.3
XYZ_t, XYZ_r = (xy_to_XYZ(
np.hstack([
Luv_uv_to_xy(illuminant_chromaticities[1:3]),
full((2, 1), as_float_scalar(Y_r)),
])) / Y_r)
xyY_cr, xyY_ct = [], []
for i, experiment_result in enumerate(experiment_results):
xyY_cr.append(
np.hstack([
Luv_uv_to_xy(experiment_result[2]),
samples_luminance[i] * Y_r,
]))
xyY_ct.append(
np.hstack([
Luv_uv_to_xy(experiment_result[1]),
samples_luminance[i] * Y_t,
]))
XYZ_cr = xyY_to_XYZ(xyY_cr)
XYZ_ct = xyY_to_XYZ(xyY_ct)
return CorrespondingColourDataset(experiment, XYZ_r, XYZ_t, XYZ_cr, XYZ_ct,
Y_r, Y_t, B_r, B_t, {})
